The present application generally relates to software-as-a-service (SaaS) system. More particularly, the present application relates to improving the resource sharing capability in a SaaS system under a constraint of performance isolation between individual tenants.
SaaS is a method for software deployment using the Internet. Under SaaS, a software provider licenses a software application to clients for use as a service on demand, e.g., through a time subscription. SaaS allows the provider to develop, host and operate a software application for use by clients, who just need a computer with internet access to download and run the software application and/or to access a host to run the software application. The software application can be licensed to a single user or a group of users, and each user may have many clients and/or client sessions.
Typically, SaaS systems are hosted in datacenters, whose infrastructure provides a set of resources and/or application services to a set of multiple tenants. A “tenant” here refers to a distinct set/group of customers/users with a service contract with the provider to support a specific IT workload. Therefore, each tenant comprises several individual users, each of whom requests his/her specific applications and/or sends service requests to the SaaS system.
Service Level Agreements (SLAs) are often used between a hosting SP (Service Provider) and hosted tenants, which specify desired performance levels to be delivered as well as the penalties to be imposed when these desired performance levels are not met. An SP can plan and allocate a resource capacity for each tenant to ensure that SLA requirements are fulfilled. This allocation may lead the SP to conservatively provision the resources in order to adequately cover tenant's peak-load requirements.
While the SP wants to maximize the sharing of the infrastructure resources in the SaaS environment, these resource-sharing arrangements can conflict with a need to ensure that each individual tenant has an availability and access to the required resources for fulfilling the tenant's SLA requirements. Therefore, while it is a priority for the SP to improve the resource sharing capability of the SaaS infrastructure, it is also important to isolate a performance impact due to the resource sharing on the individual tenants to ensure that a mutual performance impact due to the resource sharing in an SaaS environment is minimized and does not compromise the essential performance requirements (e.g., creating and/or storing on-line documents without any visible delay) of individual tenants.
Thus, when serving multiple tenants with their set of requirements in a SaaS environment, the SP needs to find a way to manage system and application resources in order to resolve this conflict between maximizing the resource sharing and minimizing the mutual performance impact. For example, some SPs may provide only a single shared resource pool for all the multiple tenants, and an operations management system is responsible for adjusting/allocating the resources in this resource pool so as to appropriately fulfill customer requests taking into account the SLA requirements and the availability and capacity of the system. However, this approach can lead to a conservative provisioning of the resources, particularly since anecdotal evidence suggests that the ratio of a peak load to an off-peak load for Internet-based SaaS applications can be of an order of 300%.
Furthermore, in a typical SaaS environment, due to variations in the number of requests and costs associated with each request, it may be difficult to estimate workload requirements in advance. Due to these variations, the worst-case resource capacity planning will invariably lead to requirements that are either unfeasible or inefficient. Although it is desirable to obtain an optimal dynamic resource allocation, this requires solving a difficult optimization problem and cannot be the basis for a practical real-time resource scheduling and resource assignment. Moreover, it is also difficult to obtain accurate predictions for the costs of different requests in a dynamic environment, which are required as input for solving this dynamic resource allocation problem. An additional complication arises in SaaS systems when new customers are being added, as the SaaS system may need to estimate the service-request costs for these new users, even when there is little or no historical or prior information on their usage patterns. As a result, the typical SaaS system cannot simultaneously fulfill the tenant SLA requirements and at the same time maximize the resource sharing capacity.